中文摘要：

为了提高显著性检测的鲁棒性,提出一种基于最优种子选取及局部平滑标签传播的显著性检测模型.首先计算初始背景图并进行优化,对优化后的背景图进行随机采样来提取最优背景种子点;然后融合2种不同方式下获得的先验图来得到物体先验图,通过对此先验图进行阈值化来提取最优前景种子点;最后基于上述提取策略得到的种子点,应用局部平滑标签传播模型预测其他区域的标签信息,从而获得显著图.在3个被广泛应用的数据集上进行定性与定量的实验结果表明,该模型能够有效地提升检测效果.

英文摘要：

To improve the robustness of salient region detection,this paper proposes a salient region detection model based on optimal seeds extraction and locally smoothed label propagation.By the model,the first step is to calculate a refined background map and extract optimal background seeds by random sampling in the map.Then an object prior map is generated by fusing two different prior maps.The optimal foreground seeds are extracted by thresholding.Finally,locally smoothed label propagation is proposed to predict labels of other regions by treating previously obtained seeds as initial labels and final saliency map is generated according to the predicted labels.Both quantitative and qualitative evaluations on three widely used datasets demonstrate the superiority of the proposed model to other several state-of-the-art models.